Question,ground_truth_answer,Sources,Custom_RAG_context,Simple,Custom_RAG_answer,llama3_answer,anthropic_claud3_haiku_answer
What do the parameters for HNSW mean?,"# M: maximum degree of nodes in a layer of the graph. 
# efConstruction: number of nearest neighbors to consider when connecting nodes in the graph.
# ef: number of nearest neighbors to consider when searching for similar vectors.
",https://milvus.io/docs/index.md,"this value can improve recall rate at the cost of increased search time. HNSW HNSW (Hierarchical Navigable Small World Graph) is a graph-based indexing algorithm. It builds a multi-layer navigation structure for an image according to certain rules. In this structure, the upper layers are more sparse and the distances between nodes are farther; the lower layers are denser and the distances between nodes are closer. The search starts from the uppermost layer, finds the node closest to the target in this layer, and then enters the next layer to begin another search. After multiple iterations, it can quickly approach the target position. In order to improve performance, HNSW limits the maximum degree of nodes on each layer of the graph to M. In addition, you can use efConstruction (when building index) or ef (when searching targets) to specify a search range. Index building parameters Parameter Description Range M Maximum degree of the node (2, 2048)","HNSW (Hierarchical Navigable Small World Graph) is a graph-based indexing algorithm. It builds a multi-layer navigation structure for an image according to certain rules. In this structure, the upper layers are more sparse and the distances between nodes are farther; the lower layers are denser and the distances between nodes are closer. The search starts from the uppermost layer, finds the node closest to the target in this layer, and then enters the next layer to begin another search. After multiple In order to improve performance, HNSW limits the maximum degree of nodes on each layer of the graph to M. In addition, you can use efConstruction (when building index) or ef (when searching targets) to specify a search range.  Index building parameters    Parameter Description Range     M Maximum degree of the node (2, 2048)","The parameters for HNSW are as follows:\n
# M: Maximum degree of the node, limiting the connections each node can have in the graph. Range is [2, 2048].\n
# efConstruction: Parameter used during index building to specify a search range.\n
# ef: Parameter used when searching for targets to specify a search range.\n","The parameters for HNSW include M, which is the maximum degree ofnnodes on each layer of the graph, and ef (construction) or ef (search), which are used to specify a search range. These parameters can be adjusted to improve performance.","The parameters for HNSW (Hierarchical Navigable Small World Graph) are:  1. M: This is the maximum degree of the nodes in the graph. It controls the 'sparsity of the upper layers and the density of the lower layers. The range for M is (2, 2048).  2. efConstruction: This parameter specifies the search range when building the index. It affects the recall rate and search time - a higher value can improve recall at the cost of increased search time.  3. ef: This parameter specifies the search range when searching for targets. Similar to efConstruction, a higher value can improve recall but increase search time."
What are good default values for HNSW parameters with 25K vectors dim 1024?,"M=16, efConstruction=32, ef=32","https://milvus.io/docs/index.md, https://milvus.io/docs/hnsw.md","parameters vary with Milvus distribution. Select your Milvus distribution first. Index building parameters Parameter Description Range nlist Number of cluster units. Number of factors of product quantization dim mod m == 0 nbits [Optional] Number of bits in which each low-dimensional vector is stored. [1, 16] (8 by default) Search parameters Common search Parameter Description Range Default Value nprobe Number of units to query [1, nlist] 8 Range search Parameter Description Range Default Value","HNSW (Hierarchical Navigable Small World Graph) is a graph-based indexing algorithm. It builds a multi-layer navigation structure for an image according to certain rules. In this structure, the upper layers are more sparse and the distances between nodes are farther; the lower layers are denser and the distances between nodes are closer. The search starts from the uppermost layer, finds the node closest to the target in this layer, and then enters the next layer to begin another search. After multiple Parameter Description Range Default Value     nlist Number of cluster units [1, 65536] 128   m Number of factors of product quantization dim mod m == 0 4   nbits [Optional] Number of bits in which each low-dimensional vector is stored. [1, 16] 8  Search parameters   Common search    Parameter Description Range Default Value     nprobe Number of units to query [1, nlist] 8  Limits on search    Parameter Range     top-K <= 1024  GPU_BRUTE_FORCE","M=16, efConstruction=500, and ef=64","For a Milvus distribution, there is no direct reference to HNSW (Hierarchical Navigable Small World) parameters. However, based on the general guidelines for Index building and Search parameters in Milvus documentation, we can infer the following default values:  * nprobe = 8 * radius = defaults to 1.",I don't know. The context provided does not contain information about good default values for HNSW parameters with 25K vectors of dimension 1024.
What does nlist mean in ivf_flat?,The `nlist` parameter in IVF_FLAT index divides a vector space into `nlist` clusters. ,https://milvus.io/docs/index.md,"index? IVF_FLAT index divides a vector space into nlist clusters. If you keep the default value of nlist as 16384, Milvus compares the distances between the target vector and the centers of all 16384 clusters to get nprobe nearest clusters. Then Milvus compares the distances between the target vector and the vectors in the selected clusters to get the nearest vectors. Unlike IVF_FLAT, FLAT directly compares the distances between the target vector and each and every vector. Therefore, when the total number","BIN_FLAT is accurate because it takes an exhaustive approach to search, which means for each query the target input is compared to vectors in a dataset. This makes BIN_FLAT the slowest index on our list, and poorly suited for querying massive vector data. There are no parameters for the BIN_FLAT index in Milvus, and using it does not require data training or additional storage.  Search parameters    Parameter Description Range metric_type [Optional] The chosen distance metric. See Supported Metrics. team_size Specifies the number of CUDA threads used for calculating metric distance on the GPU. Common values are a power of 2 up to 32 (e.g. 2, 4, 8, 16, 32). It has a minor impact on search performance. The default value is 0, where Milvus automatically selects the team_size based on the vector dimension. 0  GPU_IVF_FLAT","In IVF_FLAT, nlist refers to the number of cluster units that divide a vector space. It determines how many clusters Milvus will compare distances with when searching for nearest vectors. The range for nlist is [1, 65536], with a default value of 128.",The `nlist` parameter in IVF_FLAT index divides a vector space into `nlist` clusters.,"The nlist parameter in the IVF_FLAT index in Milvus refers to the number of cluster units that the vector space is divided into. The default value for nlist is 16384, which means the vector space is divided into 16384 clusters. Milvus then compares the distances between the target vector and the centers of all 16384 clusters to get the nprobe nearest clusters, and then compares the distances between the target vector and the vectors in the selected clusters to get the nearest vectors."
"What is the default AUTOINDEX distance metric in Milvus Client?
","Trick answer:  IP inner product, not yet updated in documentation still says L2.",https://milvus.io/docs/index.md,and Hamming. This type of indexes include BIN_FLAT and BIN_IVF_FLAT. Indexes for sparse embeddings The distance metric supported for sparse embeddings is IP (Inner Product) only. The types of indexes include SPARSE_INVERTED_INDEX and SPARSE_WAND. The following table classifies the indexes that Milvus supports: Floating-point embeddings Binary embeddings Sparse embeddings Supported index Classification Scenario FLAT N/A Relatively small dataset Requires a 100% recall rate IVF_FLAT Quantization-based index,"Parameter Description Range Default Value     nlist Number of cluster units [1, 65536] 128  Search parameters   Common search    Parameter Description Range Default Value     nprobe Number of units to query [1, nlist] 8 Range search    Parameter Description Range Default Value IVF_FLAT index divides a vector space into nlist clusters. If you keep the default value of nlist as 16384, Milvus compares the distances between the target vector and the centers of all 16384 clusters to get nprobe nearest clusters. Then Milvus compares the distances between the target vector and the vectors in the selected clusters to get the nearest vectors. Unlike IVF_FLAT, FLAT directly compares the distances between the target vector and each and every vector.",L2,"According to the Milvus documentation, the default AUTOINDEX distance metric in Milvus Client is L2 (Euclidean) distance.",The default AUTOINDEX distance metric in Milvus Client is L2 (Euclidean distance).